Polyolefin reactor systems typically rely on periodic or intermittent measurements of resin properties. The measurements are generally determined from a resin sample, which is extracted from the process system. Real-time measurements of polyolefin resin quality may be subject to substantial errors due to different reasons. These errors may be due to sampling issues, laboratory procedure misapplications, equipment malfunctions, or other causes. If the resin property measurements have substantial error, and then are subsequently used for reactor control, significant process disruption may be caused due to a response based on these improper measurements. However, if the sampled measurements can be marked as “likely erroneous,” then it may be acted upon accordingly, and significantly better reactor control may be maintained. Currently, an operator decides if laboratory readings are appropriate for entry into the automation system, or for use in manual operation. This approach is often not effective due to the complexity in determining the feasibility of the reading. The complexity and uncertainty in the existing methodology results in operator decisions which are not sufficiently timely, consistent, or accurate. The improvement sought herein is to flag polymer property readings as acceptable or unacceptable before such measurements being used to manipulate reactor variables and conditions.
U.S. Pat. No. 5,260,882 describes a process for estimation of properties utilizing experimental information using constraints determined by chemical kinetics, statistical thermodynamics and molecular mechanics including experimental information on proposed polymeric or copolymeric substances of large molecules for the estimation of the physical properties of the substances by first defining the substances molecular chemical composition, second, estimating properties of the molecular chemical composition when 3-dimensionally folded, third, forming the composition into a polymeric cluster, fourth, estimating the physical properties of the polymeric cluster, and finally preparation of the polymeric substances having the properties as estimated.
U.S. Pat. No. 5,550,630 describes a method for analyzing the structures of chemical organic compounds, polymers, polynucleotides and peptides. The method uses the integrated intensity of spectral light absorption in wide or narrow regions of the ultraviolet and/or visible spectrum and relates these parameters additively to the structural characteristics of the analyzed chemical compound. For the analysis of polymers, nucleotides and/or peptides the integrated intensities of spectral absorption are used sequentially in narrow regions of the ultraviolet light which enables the determination of the molecular weight and the complete amino acid composition of the analyzed compound. All these procedures are interconnected in an automatic spectrophotometric structural analyzer.
U.S. Pat. No. 6,406,632 describes rapid characterization and screening of polymer samples to determine average molecular weight, molecular weight distribution and other properties is disclosed.
U.S. Pat. No. 6,687,621 describes a computational method for predicting a desired property and/or performance of polymers, and/or identifying and designing polymers that provide the desired property and/or performance, wherein the desired property can be provided by the neat, undiluted polymers, or diluted polymers in a composition. The method is a QSAR approach wherein the descriptors used are structural descriptors which are experimentally generated and/or derived from one or more analytical methods.
Despite the research efforts in developing polymer property prediction models, there is still a need for a method for improving the prediction of polymer properties and a system having improved polymer property prediction capabilities. Furthermore, there is a need for a method for automatically determining the feasibility of laboratory measurements using (1) the expected standard deviation of the sample polymer property measurements and/or (2) process models and estimates of measurement uncertainty.